Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview

Abstract

Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until liver damage is irreversible. Given this complex situation, the search for noninvasive alternatives is clinically important. A hallmark of NAFLD progression is the dysregulation in lipid metabolism. In this context, recent advances in the area of machine learning have increased the interest in evaluating whether multi-omics data analysis performed on peripheral blood can enhance human interpretation. In the present review, we show how the use of machine learning can identify sets of lipids as predictive biomarkers of NAFLD progression. This approach could potentially help clinicians to improve the diagnosis accuracy and predict the future risk of the disease. While NAFLD has no effective treatment yet, the key to slowing the progression of the disease may lie in predictive robust biomarkers. Hence, to detect this disease as soon as possible, the use of computational science can help us to make a more accurate and reliable diagnosis. We aimed to provide a general overview for all readers interested in implementing these methods.

Afamin Levels and Their Correlation with Oxidative and Lipid Parameters in Non-diabetic, Obese Patients

“…The role of fat-soluble, herbal molecules, frequently referred to as vitamin E, has been studied for decades [ 28 ]. It is widely established that free radicals—produced during increased oxidative stress in inflammatory conditions—cause lipid peroxidation and thus contribute to atherosclerosis [ 29 ]. The antioxidant properties of α- and γ-tocopherols—the two most common and widely consumed vitamin E components—have been well described [ 30 , 31 ].…”

Measurement of lipid flux to advance translational research: evolution of classic methods to the future of precision health

“…Data science, artificial intelligence, and machine learning Two computer-based techniques that will significantly influence the future of lipid research are artificial intelligence (AI), which enables computers to mimic the human decision-making process, and machine learning (ML), in which data analysis includes automated, analytical model building. These techniques have already improved mass spectrometry software through the detection of chromatographic peaks by improvements in noise filtration 57 and have been extended to the field of medicine in which ML of lipidomic data is proposed to improve the diagnosis of fatty liver disease 58 . A next step in this field will include the integration of lipidomics with genetic information.…”

Metabolomics in Bariatric and Metabolic Surgery Research and the Potential of Deep Learning in Bridging the Gap

“…More recently, Perakakis et al have implemented support vector machine, a supervised learning algorithm, to aid in the diagnosis of nonalcoholic steatohepatitis and fibrosis by noninvasive means of metabolomics [ 31 ]. Along the same lines, Castañé et al investigated the potential of coupling machine learning and lipidomics in order to decipher the metabolic dysfunctions underlying fatty liver disease [ 32 ].…”